An approach to one-class extraction from remote sensing imagery
by Shukui Bo; Yongju Jing
International Journal of Information and Communication Technology (IJICT), Vol. 11, No. 1, 2017

Abstract: One-class extraction tries to distinguish a specific class of interest from the remainder of the data set. The one-class extraction problem can be solved by either a one-class classifier or a multi-class classifier. With a multi-class classifier, the one-class extraction of remote sensing image is studied in this article. First, we analyse the probability of error for one-class extraction with nearest neighbour classifier. As a non-parametric method, the nearest neighbour classifier requires the data distribution to be partitioned into only two classes, the class of interest and the remainder. Second, with the two-class partitioning of the dataset, the specific class of interest is well extracted from a remotely sensed image. This study improves the classification process and would be helpful for one-class extraction of remote sensing imagery with multi-class classifiers.

Online publication date: Fri, 28-Jul-2017

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